Personalizing default rules and disclosure with Big Data.

This Article provides the first comprehensive account of personalized default rules and personalized disclosure in the law. Under a personalized approach to default rules, individuals are assigned default terms in contracts or wills that are tailored to their own personalities, characteristics, and past behaviors. Similarly, disclosures by firms or the state can be tailored so that only information likely to be relevant to an individual is disclosed and information likely to be irrelevant to her is omitted. The Article explains how the rise of Big Data makes the effective personalization of default rules and disclosure far easier than it would have been during earlier eras. The Article then shows how personalization might improve existing approaches to the law of consumer contracts, medical malpractice, organ donation, inheritance, landlord-tenant relations, and labor law.

The paper makes several contributions to the literature. First, it shows how data mining can be used to identify particular personality traits in individuals, and these traits may in turn predict preferences for particular packages of legal rights. Second, it proposes a regime whereby a subset of the population ("guinea pigs") is given a lot of information about various contractual terms and plenty of time to evaluate their desirability, with the choices of particular guinea pigs becoming the default choices for those members of the general public who have similar personalities, demographic characteristics, and patterns of observed behavior. Third, we assess a lengthy list of drawbacks to the personalization of default rules and disclosure, including cross subsidization, strategic behavior, uncertainty, stereotyping, privacy, and institutional-competence concerns. Finally, we explain that the most trenchant critiques of the disclosure strategy for addressing social ills are really criticisms of impersonal disclosure. Personalized disclosure not only offers the potential to cure the ills associated with impersonal disclosure strategies, but it can also ameliorate many of the problems associated with the use of personalized default rules.

Law is impersonal. The state generally does not tailor the contents of the law to people's characteristics and traits. In this Article, we argue that in the era of Big Data, law should become more personalized. Our main focuses are default rules (situations where people face a choice between sticking with a default option or specifying a different option instead) and disclosure (where the law mandates that individuals receive particular information). Our claim has important applications to contract law, consumer law, inheritance law, medical malpractice, property law, labor law, privacy law, organ donation law, and other fields.

Let us illustrate our approach with an example from inheritance law. Empirical research has shown that married fathers are more likely than married mothers to bequeath all their property to their spouses (55% compared to 34%). (1) Moreover, according to these studies, men bequeath significantly larger shares of their estates to their spouses (80% of estates are willed to widows versus 40% to widowers). (2) These data are consistent with rational-choice models of behavior: wives trust their husbands less than husbands trust their wives to use inherited resources in the best interests of their mutual children, since men are significantly more likely to remarry and devote resources to the children from their second marriage, which comes at the expense of children from their first marriage. (3)

If men's testamentary preferences differ systematically from women's, why should intestacy laws continue to be gender neutral? (4) Why not have different default intestacy rules for men and women instead? We argue that as long as these preferences remain stable and gender correlated, a different set of default rules for women would lead in the long run to more estate resources being allocated to heirs according to decedents' true preferences. We further posit that it may be desirable to use other readily observable characteristics (e.g., wealth, health, time of marriage, age of children, and occupation) that could predict default rules in intestacy for population subgroups. As with any default rules, individuals would be free to alter these defaults by executing a will. (5)

We also advocate a more ambitious version of personalization here, one that would let courts determine how an intestate's estate should be allocated based on an analysis of his consumer behavior during his lifetime. In the era of Big Data, (6) we suggest that it will be possible to find individuals whose observable behavior and characteristics closely match those of the intestate--we refer to these people as "guinea pigs"--to examine the kinds of choices that the guinea pigs made in their wills and then to use these choices as a template for determining what the intestate likely would have wanted. (7) An upshot of widely employing this approach is that more estates would be allocated in a way that better approximates the true preferences of the decedent. Given the fact that most individuals leave no wills, this advantage could be significant. Furthermore, with detailed intestate defaults, many individuals who would have otherwise needed to incur the expenses of drafting wills now may no longer need to do so. After all, they will recognize that even in the absence of a written will, their intestacy rules will be personalized and hence will more closely approximate what they would have wanted than will the status quo's one-size-fits-all approach.

We are not the first to raise the possibility of using personalized default rules. Recently, Cass Sunstein offered a provocative assessment of existing, impersonal default rules and suggested two alternatives to them: active choices and personalized default rules. (8) Sunstein's work continues a conversation begun by Ian Ayres, who first argued that default rules could be "tailored" to market conditions or the attributes of parties. (9) This conversation was extended by George Geis, who modeled tailored and untailored default rules under particular sets of assumptions to analyze the welfare implications of trading off precision against complexity. (10)

Sunstein's bottom line is that "personalized default rules are the wave of the future. We should expect to see a significant increase in personalization as greater information becomes available about the informed choices of diverse people." (11) We agree wholeheartedly and regard his contribution to the literature as significant. He astutely notes that the appeal of personalized default rules depends on the heterogeneity among a given population, the state's access to information about individuals' preferences and its ability to create a structure conducive to rational choices, the richness of the data available about individual preferences, and the transaction and confusion costs associated with prompting parties to a transaction to make active choices about the parameters of a deal. (12) He inventively envisions personalized default rules in contexts like the choice of retirement plans, cell phone plans, mortgages, and other settings. (13) That said, Sunstein's discussion of personalized default rules is truncated--it is a short part of a short essay. He has not addressed the question of how courts would apply personalized default rules. And the earlier work by Ayres and Geis explicitly lumps together default rules that are tailored based on both contracting parties' characteristics and market conditions, focusing--in the abstract--on the costs of promulgating and adjudicating tailored default rules. (14)

No scholars have previously offered a comprehensive theory of personalized default rules, nor has anyone explored in detail the feasibility of such an approach. In this Article, we will develop such a theory, show its feasibility in the real world, and point out what legislatures and courts should do in order to make a personalized default-rule regime implementable in many fields. In particular, we will show that with a bit of innovative tweaking, tools developed in the age of Big Data can facilitate providing certainty around the meaning of default terms for heterogeneous individuals and firms. By mitigating the uncertainty associated with the development of personalized default rules, Big Data can make personalization far more appealing than it was in previous information environments.

The Article proceeds as follows. Part I explores the existing conceptions of default rules and identifies the dominant strategies for supplying such rules: majoritarian default rules and minoritarian (penalty) default rules. It then shows how each type of default rule might be improved via personalization, such that the content of the rule in question will differ among heterogeneous individuals. In this Part, we illustrate our claims mostly through consumer contracts and point out the main considerations that could make the...